do perceptions become reality? the moderating role of supply chain resiliency on disruption...

JOURNAL OF BUSINESS LOGISTICS, Vol. 31, No. 2, 2010 1

DO PERCEPTIONS BECOME REALITY? THE MODERATING ROLE OF SUPPLY CHAIN RESILIENCY ON DISRUPTION OCCURRENCE

by

George A. Zsidisin Bowling Green State University

and

Stephan M. Wagner Swiss Federal Institute of Technology Zurich, Switzerland

INTRODUCTION

Issues associated with risk and continuity in the supply chain have received considerable attention in both the practitioner and academic communities. For example, the Institute for Supply Management (ISMTM) changed their moniker to “Maximizing Opportunity, Managing Risk” (www.ism.ws). The Council of Supply Chain Management Professionals (CSCMPTM) has recently hosted various seminars and programs associated with risk and security in the supply chain (www.cscmp.org). From the academic side, journals such as the International Journal of Physical Distribution and Logistics Management and Production and Operations Management have published special issues associated with risk in the supply chain. Further, scholarly books such as Supply Chain Risk (Brindley 2004), Supply Chain Risk Management: Minimizing Disruptions in Global Sourcing (Handfield and McCormack 2007), and Supply Chain Risk: A Handbook of Assessment, Management, and Performance (Zsidisin and Ritchie 2009) have emerged in the literature.

Although there is increasing concern and study occurring in the field of supply chain risk and continuity, the

majority of empirical studies to date have appropriately focused on using qualitative research techniques, such as the case study research method (Yin 2009) to provide initial insights and to build theory (Eisenhardt 1989; Ellram 1996) for understanding risk in the supply chain. However, these studies have had some limitations, specifically with regard to generalizability, and the current dearth of theory and hypothesis testing.

Risk, which is defined as “…the extent to which there is uncertainty about whether potentially significant

and/or disappointing outcomes of decisions will be realized” (Sitkin and Pablo 1992, p. 10), exists throughout firms’ supply chains, and its investigation can be viewed from a downstream, customer-facing perspective, internal to the firm, or from a supplier-oriented viewpoint (Sheffi 2005; Tang 2006). The study of supply chain risk and disruptions has focused on various subsets of this phenomenon, such as how it is perceived (Mitchell 1995; Zsidisin 2003), managed (Chopra and Sodhi 2004; Hallikas et al. 2004; Harland, Brenchley, and Walker 2003; Ritchie and Brindley 2007), and its effects on organizational performance (Hendricks and Singhal 2003, 2005a, 2005b; Norrman and Jansson 2004; Wagner and Bode 2008). One subset of the supply chain risk literature is the study of how firms can become resilient to the threats and occurrence of supply chain disruptions (Christopher and Peck 2004; Sheffi 2005; Sheffi and Rice 2005; Tang 2006).

The purpose of this study from a supply management (purchasing) perspective with regard to supply disruptions

is two-fold. The first purpose of this article is to examine the relationship between how supply management professionals perceive various sources of risk and how often that risk occurs in the form of supply disruptions. The second purpose of the article is to test the extent to which supply chain resiliency practices moderate disruption frequency.

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The structure of the article is as follows. First, we provide a background into prior research into perceptions of supply-side risk sources, supply disruption effects, and supply chain resiliency in the literature review. Next, we develop the main effect and interaction effects hypotheses tested in this study. A description of the research method employed in this study is then provided, to include data collection procedures and the survey instrument and measures. This is followed by a discussion of the data analysis and research findings. Managerial implications, limitations and conclusions are then provided.

LITERATURE REVIEW Perceptions of Supply-side Risk Sources

Supply disruptions stem from a broad range of risk sources and can emerge from within the supply chain or from external events. For instance, the financial default of a supplier and a natural disaster destroying the facilities of a second-tier supplier in a low-cost country are situations with different attributes, and therefore may entail different effects on a focal firm. Addressing this issue and attempting to differentiate supply chain risks from other business risks, many scholars have proposed classifications in the form of typologies and/or taxonomies that are often labeled as “supply chain risk sources” (Christopher and Peck 2004; Hallikas et al. 2004; Jüttner 2005; Spekman and Davis 2004). For example, Svensson (2000) named two categories (quantitative and qualitative), Jüttner (2005) delineated three categories (supply, demand, and environmental), and Chopra and Sodhi (2004) proposed nine categories (disruptions, delays, systems, forecast, intellectual property, procurement, receivables, inventory, and capacity) of supply chain risk sources. Since our study focuses on risks to be handled by supply management (unlike the above mentioned classifications which address supply chain risks per se), we need to specify risk sources that are “the result of a supply chain disruption that emerged from the supply-side risk source” (Wagner and Bode 2006, p. 303) in more detail.

In this article we investigate if the supply management professionals’ perceptions of various categories of

supply-side risks are related to supply disruptions, and how they respond to these perceptions with respect to the implementation of supply chain resiliency practices. Therefore, it is warranted to shed light into the notion of “perception,” which is the product of the supply management professionals’ unconscious inference in the context of supply risk.

Risk perceptions are “…based on how information on the source of a risk is communicated, the psychological

mechanisms for processing uncertainty, and earlier experience of danger. This mental process results in perceived risk: a collection of notions from which people form their own risk sources relative to the information available to them and their basic common sense” (Renn 2004, p. 104). What supply management professionals perceive about supply-side risk sources is their understanding as to what might detrimentally affect inbound supply, and the financial effects those events can have on their firms’ performance. Their view about supply-side risk sources and their potential for supply disruption is based on the information available plus their prior experience. Individuals’ ability to judge probabilities and make projections in uncertain decision-making environments is bounded (Miller 1956; Thaler 1985). This may also restrict a supply management professional’s ability to examine the likelihood of supply disruption occurrence stemming from various supply-side risk sources (Carter, Kaufmann, and Michel 2007). As a consequence, their decisions concerning whether or not to implement certain supply risk management practices are based on their beliefs and subjective probabilities regarding the likelihood that their firm will be affected by certain supply-side disruptions as well as the magnitude of the risk (Slovic, Fischhoff, and Lichtenstein 1982).

Despite the subjectivity of whether and how severely a potential supply disruption might affect the firm, we

assume and will show in this article that supply management professionals have a good understanding of their firm’s propensity to supply-side disruptions. This is because they apply heuristics (e.g., representativeness heuristic) (Kahneman and Tversky 1972; Watson and Rodgers 1998) and aim for improving their decision-making, such as through methods of decision analysis (Bazermann 2005; Keren 1990) in order to make more qualified judgments pertaining to supply uncertainties. Supply management professionals will be driven by these judgments on which they base their actions and derive supply risk management strategies and activities accordingly.


Effects of Supply Disruptions

Anecdotal and case-study based evidence strongly indicates that the disruption of the supply chain can have severe negative consequences for the focal firm connected with other firms in the supply chain. The well-known example of the Albuquerque chip-plant fire illustrates this. In 2000, a fire destroyed the entire production capacity of a chip-plant of Philips Electronics in Albuquerque, a sub-supplier of the Scandinavian cell phone makers Nokia and Ericsson, for several weeks. Ericsson (with limited capacity in supply risk management at that time) incurred a loss of over US $ 400 million (Norrman and Jansson 2004; Sheffi 2005).

Large-scale empirical investigations of the risk–performance link in the context of supply chain and supply risk

has just begun to emerge. In their event study Hendricks and Singhal (2003, 2005a, 2005b) analyzed announcements of supply chain glitches and the impact of supply chain disruptions on shareholder value (Hendricks and Singhal 2003, 2005a) and on operating performance (e.g., sales, operating income, return on assets) (Hendricks and Singhal 2005b). They show that firm operating performance erodes and that capital markets penalize firms who experience supply chain disruptions. Wagner and Bode (2008) investigate the relationship between several supply chain risk categories and supply chain performance. They show that supply-side risks (which include supplier quality problems, supplier delivery problems, supplier defaults, and supply market shortages) have a significant negative impact on supply chain performance (delivery dependability, delivery speed, order fill capacity, and customer satisfaction).

Supply Chain Resiliency

Risk management in firms can be defined as any “field of activity seeking to eliminate, reduce and generally control pure risks” (Waring and Glendon 1998, p. 3). Typical risk management processes for enterprise risks in general (Damodaran 2007; Waring and Glendon 1998) and supply and supply chain risks in particular (de Waart 2006; Hallikas et al. 2004; Sheffi and Rice 2005), encompass the stages of (1) risk identification, (2) risk analysis (including risk assessment and classification), (3) risk management in the narrow sense (i.e., risk treatment), and (4) risk monitoring. With such an approach, firms try to determine, implement, and monitor an optimal mix of activities to avoid, defer, reduce, or transfer all relevant risks. A firm’s remaining risk exposure should be in line with the firm’s risk preference and corporate strategy. For the purpose of this study, we are interested in specific risk management practices which supply management professionals can implement to create resiliency to potential supply disruptions.

Supply chain resiliency consists of the ability to return to normal performance levels following a supply chain

disruption (Sheffi 2005). Supply chain resilience can be created by building in redundancy or through flexibility (Christopher and Peck 2004; Sheffi and Rice 2005). Pursuing a redundancy strategy in managing supply chain risk focuses on limiting or mitigating the negative consequences of a disruption by keeping resources in reserve, such as having safety stock, maintaining multiple suppliers, and running operations at low capacity utilization rates (Sheffi and Rice 2005; Trevelen and Schweikhart 1988). Although redundant supply chain practices can “buy time” for a firm to recover from a disruption, there are associated costs with this strategy, such as tying capital into inventory and additional transaction costs from managing multiple suppliers.

Supply chain flexibility, on the other hand, consists of building organizational and interorganizational

capabilities to sense threats to supply continuity and to respond to them quickly (Sheffi and Rice 2005). These practices can serve to both bolster organizational resilience to disruptions as well as create a competitive advantage in the marketplace by better meeting customer demand changes. Supply chain flexibility consists of five facets: (1) supply and procurement; (2) conversion; (3) distribution and customer-facing activities; (4) control systems; and (5) corporate culture (Sheffi and Rice 2005). Since this study focuses on one source of supply disruptions—supply risk—our orientation of supply chain flexibility will center on purchasing and supply practices that facilitates flexibility. The next section will provide the rationale for understanding the linkages between supply risk perceptions, disruption occurrence, and supply chain resiliency.

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HYPOTHESES Supply Risk Perceptions and Disruption Occurrence

One purpose of this study is to investigate the relationship between management perceptions of supply risk and the frequency of experiencing the effects of supply disruptions. Here we seek to determine if risk perceptions become reality, that is, if supply management professionals’ judgments about risk stemming from various supply-side risk sources are accurate and materialize in disruptions.

Supply-side risk sources have previously been classified in various ways, such as by supplier, market, and item

characteristics (Zsidisin 2003). This study adopts and adds to this classification by investigating the perceptions of supply-side risk sources that emanate from suppliers, supply markets, and characteristics associated with extended supply chains that become risk drivers. From these supply-side risk sources, negative consequences for the focal firm or the supply chain as a whole can emerge.

Risks from the supplier consists of issues and problems that can arise from the current supplier portfolio of the

buying firm, to include factors that affect the interactions between the two organizations. These can include issues such as supplier financial instability, the effectiveness of the supplier’s management team, and the communication processes between the buyer and supplier organizations. These risk sources or drivers can lead to supply disruptions if the buying firm does not directly intercede in managing that risk (Wagner and Bode 2008). For example, if a supplier becomes bankrupt, that firm may not be able to meet all of its customer requirements in the short-term, and will not meet any customer requirements if it eventually goes out of business.

Risks coming from the supply market consist of issues and problems beyond the scope of a single supplier or

buyer-supplier relationship. This includes supply market structures and configurations (i.e., monopolies, cartels), the environment that the firms in these markets compete in, the number of qualified suppliers that exist, as well as capacity availability (overcapacity vs. shortages) (Carbone 2000; Sawyer 2005). This source of supply risk is often beyond the control of both the supplier and buying firm.

Many firms obtain products or services from sources that extend far beyond their respective locations. As a

consequence, a third important supply-side risk source comes into play when firms extend their supply chains. Risk from the extended supply chain can result in supply disruptions due to heightened uncertainty associated when sourcing from suppliers in locations further away from the firm. Extended supply chain issues have their own specific problems that transcend beyond market characteristics, such as political instability when sourcing in certain regions of the world (Schoenherr, Tummala, and Harrison 2008; Vestring, Rouse, and Uwe 2005), greater transportation uncertainty (Manuj and Mentzer 2008; Wagner and Bode 2006), and costs that can arise from increased complexity (Craighead, Blackhurst, Rungtusanatham, and Handfield 2007; Zeng and Rosetti 2003). Therefore, in this study we pay particular attention to issues and potential problems associated with extended supply chains.

The three categories of supply-side risk in this study—supplier, supply market, and extended supply chains—

can result in buying firms experiencing supply disruptions. Therefore, we propose that as supply management professionals perceive greater threats associated with the three risk sources, that it will result in experiencing the effects of supply disruptions more frequently, as shown in H1a-c below:

H1a-c: Supply management professionals perceiving risk from (a) suppliers, (b) supply markets, and (c)

extended supply chains, experience the effects of supply disruptions more frequently.

Intervening Effects of Supply Chain Resiliency

The second major goal of this research is to investigate if the use of various supply chain resiliency practices serves a moderating role in reducing the frequency with which buying firms experience the effects of supply disruptions. Although we propose that the greater the extent that supply-side risk sources (drivers) exist, the more likely that various effects of supply disruptions will occur, we further believe that supply management professionals are also likely to intervene in some manner to address those threats to supply continuity and create more resilient firms and supply chains.


As discussed in the literature review section, supply chain resiliency consists of building flexibility and redundancy to offset supply disruptions. The sensing of potential supply disruptions is a key element of supply chain flexibility (Sheffi and Rice 2005). From a supply management perspective, firms can improve their ability to detect potential disruptions through activities such as auditing, monitoring, and certifying suppliers. For example, auditing and monitoring suppliers can include selecting and constantly evaluating the financial viability of those firms to avoid the consequences of supplier default, insolvency, or bankruptcy (Milne 2009; Wagner and Johnson 2004). Flexibility can also be bolstered by creating strong buyer-supplier relationships, when appropriate (Sheffi 2005). The long-term orientation of closer relationships can motive suppliers to take extraordinary measures to find ways of meeting their customer requirements, essentially “back[ing] them up” (Sheffi 2005; p. 210) when disruptions occur. Activities such as certifying suppliers can serve as an initial step in determining how purchasing firms should pursue relationships with key suppliers.

Pursuing supply chain practices that build in redundancy allows firms to reduce the effects of a supply

disruption A usual approach is to anticipate risk scenarios and build slack into the supply chain (Christopher and Peck 2004; Sheffi and Rice 2005). Contingency supply sources can be solicited to decrease the vulnerability to supply-side disruptions (Sheffi and Rice 2005). Also, buying firms can diversify order quantities and hedge against the sudden demise of a single supplier by having multiple competing suppliers (Tomlin 2006). Further, if business continuity plans or recovery plans are in place and executed after a supply-side disruption occurs, the negative consequences for the firm may be reduced (Gilbert and Gips 2000; Zsidisin, Melnyk, and Ragatz 2005). In sum, we propose that supply management professionals will employ supply chain resiliency practices to manage supply-side risk when it is perceived, and that those practices will result in dampening the overall effects of supply disruptions, which leads to the next hypothesis:

H2a-b: Supply management professionals that create supply chain resiliency through (a) flexibility and (b)

redundancy in response to risk perceptions, experience the effects of supply disruptions less frequently.

The overall research model examining the direct effects of supply management professionals’ perceptions of

risk on the frequency of supply disruption occurrence and the moderating effect of supply chain resiliency practices are depicted in Figure 1.

FIGURE 1

RESEARCH MODEL

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RESEARCH METHOD Data Collection

The research study involved developing and administering an on-line supply risk audit instrument from a convenience sample of supply management professionals employed at five organizations. Dillman’s Tailored Design Method was used to guide the distribution of the survey (Dillman 2007). The Chief Purchasing Officer (CPO—the highest supply management executive in a firm or business unit) for each organization was solicited for involvement. General descriptions of each firm can be found in Table 1. Please note that code names are used to ensure confidentiality.

The survey was deployed on a web server at one of the Universities, and invitations to respond were sent out by

the respective CPO’s to their supply management (purchasing) workforce, addressed by name of respondent. The invitations included an introduction of the joint research project by the company and the University-based research team, and a specific request to respond from the CPO. The invitation also shared the perceived value of the research to the company, including receipt of written reports to aid the company in assessing the state of its purchasing practices relative to supply risk. Reminder emails were sent out to all companies within 2-3 weeks of the first email, except Company MHE, which had such a high initial response rate that the CPO did not initiate a reminder email. Since the response rates for all firms were high or very high, and since little pressure was necessary to induce such high response rates, the concern of nonresponse is minimal. Nevertheless, we checked the profiles of the firms’ supply management professionals who responded against the nonrespondents and found no significant differences.

Each company received two rewards for participating: (1) a copy of their specific results, with full data analysis

by the research team, along with summary results from all participating companies for comparison (all done anonymously); and (2) each company was allowed to include up to four company specific questions of interest to the CPO to be added to the academic survey, and the resulting data analysis would include reporting back to the company the results of these additional company-specific questions.

TABLE 1

COMPANY DEMOGRAPHICS AND RESPONSE RATES

Name (Code) Structure Industry Home

country Survey

timeframe Sample Responses Response rate

Build Corporate, group and division

Home construction and improvement materials

U.S. October 2005

156 53 34.0 %

Construction Corporate plus four divisions

Home construction and improvement materials

U.S. July 2006

56 34 60.7 %

Equip Corporate plus four divisions

Paper and other capital equipment

Germany August 2006

41 33 80.5 %

Aircraft Division Aircraft manufacturer

U.S. October/ November 2006

201 141 70.1 %

MHE Corporate plus nine divisions

Material handling equipment

Germany December 2006

45 35 77.8 %

Total 499 296 59.3 %


Survey Instrument, Unit of Analysis, and Measures

The survey instrument and measures were developed in several stages. First, a preliminary questionnaire was drafted on the basis of prior research. Second, the survey instrument was pre-tested first by academic experts and then by experienced purchasing executives. During the deployment of each survey to a particular company, the survey was again pre-tested by the CPO prior to distribution in order to clarify use of terminology and to ensure consistency with business practices and language clarity. Third, at least one member of the research team met personally, and communicated via telephone and email several times, with each company’s representative to review the survey prior to deployment. In the case of the German companies, a German academic member of the research team also pre-tested the survey for language and cultural differences prior to submission to the company, and then again participated in the review with the company for clarity and appropriateness of the questions (Schaffer and Riordan 2003). No significant issues were uncovered during this process.

The questions used in the survey instrument asked respondents to report their answers with respect to a specific

purchase they managed. Thus, the unit of analysis in this study is the risk associated with a specified purchased item, and not the firms’ practices in general. This focus on the product-level allowed us to investigate specific supply-side risk sources, supply chain resiliency, and disruption occurrence for which the supply management professionals are knowledgeable and held responsible. Multiple-item measures were used to assess the focal constructs on 5-point scales. Descriptions of the specific measures and items used in this study are reported in the Data Analysis and Findings section.

In order to eliminate factors that may influence or confound the relationship between supply-side risk sources

and disruption occurrence, we controlled for two undesirable sources of variance. First, given that this study includes data from five different firms, we are controlling for possible organizational effects (i.e., dependencies between observations from one organization) in our analysis. Specifically, we have created four dummy variables to control for potential firm differences. Following the procedure suggested by Cohen, Cohen, West, and Aiken (2003, pp. 303-307), the responses from Company Construction were coded as one for the variable ‘Construction;’ responses from Company Equip were coded as one for the variable ‘Equip;’ responses from Company Aircraft were coded as one for the variable ‘Aircraft;’ and responses from Company MHE were coded as one for the variable ‘MHE.’ Thus, responses from Company Build were used as the base. Second, the degree of global sourcing might have an impact on the risks that a company faces (Wagner and Bode 2006). Since our analysis would be diluted as a result of the respondents’ varying degrees of global sourcing activities for the purchased items, we included a control variable which accounts for the purchasing volume for the specified items that comes from non-domestic sources. It was measured with a single item asking respondents “approximately what percentage of your purchase volume for this item comes from domestic sources?”

DATA ANALYSIS AND FINDINGS Factor Analyses

For the identification of parsimonious structures of supply-side risk sources, supply chain resiliency, and supply disruption occurrence, to determine underlying superordinate dimensions, we used principal component factor analysis to extract the factors. To enhance interpretation, the factor matrices were rotated using the orthogonal Varimax rotation. The solutions were obtained by rotating all factors with eigenvalues greater than one. Furthermore, reliability tests were performed for each factor using Cronbach’s alpha (Cronbach 1951).

Supply-side risk sources. There were 14 items examined for supply-side risk sources, based on the perceptions

of supply management professionals in evaluating concerns associated with their respective purchases. From the factor analysis, three factors emerged: supplier; supply market; and extended supply chains. All item loadings were greater than 0.60. The supplier risk source consists of potential problems associated with a specific supplier, its management, and systems. These include issues with the financial stability of the supplier, their management team, electronically sharing information, and problems with suppliers interpreting requirements. Supply market risk sources entail potential problems that exist with the supply market in general—not necessarily only with a specific supplier. These factors may or may not be controlled by the supplier or buying firm, but can still result in supply disruption occurrence. Issues associated with supply market risk include the lack of alternative suppliers, inability to

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influence suppliers, and inability to meet significant volume increases. Although this last item can be interpreted as a risk source from a supplier, significant demand increases often originate from the overall demand placed in a respective industry or market that can affect all suppliers, especially in commoditized markets. For example, significant demand increases for polysilicon from 2004-2006 affected the entire supply market (Winegarner and Johnson 2007), which can have a subsequent influence on taxing individual supplier capacity levels. The third supply-side risk source factor, extended supply chain, consists of characteristics associated with sourcing from suppliers in locations that are a far proximity to the buying firm’s operations. This includes issues such as political instability, long distances, transportation disruptions, and variability in transportation times. The three factors extracted explain 63.4 % of the variance. The results of the rotated factor matrix for supply-side risk sources are summarized in Table 2. The Cronbach alpha reliability estimates for the factors supplier, supply market, and extended supply chains were 0.832, 0.760, and 0.868, respectively. These results demonstrate sufficiently high reliability of the supply-side risk source scales (Hair et al. 2006, p. 137).

TABLE 2

FACTOR ANALYSIS FOR SUPPLY-SIDE RISK SOURCES

Items Supplier Supply market

Extended supply chain

Ineffective management in the supplier firm 0.830 0.082 0.216 Financial instability or financial failure of a supplier 0.811 0.218 0.059 Suppliers incorrectly interpreting our requirements 0.713 0.300 0.185 Incoming product quality problems 0.636 0.117 0.443 Labor / management problems at suppliers 0.623 0.224 0.382 Problems in electronically sharing information (e.g., through EDI, ERP) with suppliers 0.620 0.262 0.119

Lack of alternative suppliers 0.099 0.804 0.121 Inability to influence suppliers 0.254 0.711 0.301 Inability of supplier to meet significant (>20 %) increases in required volumes 0.332 0.698 0.107

Transportation disruptions with inbound supply channels 0.134 0.307 0.809 Variability in transportation times with inbound supply channels 0.131 0.355 0.777 Political instability / war affecting suppliers’ operations 0.341 -0.018 0.694 Natural disasters or “acts of God” affecting suppliers’ operations 0.283 -0.070 0.676 Long physical distances between you and your suppliers 0.123 0.285 0.658

Cronbach 0.832 0.760 0.868 Response cue: The next set of questions relates to your perception of supply risk for the purchased item you just described. When making sourcing or supply management decisions for this product, to what extent are you concerned about each of the following factors which may contribute to supply risk? (5-point scale: 1 = not at all, 2 = slightly, 3 = moderately, 4 = very, 5 = extremely). Extraction method: Principal component, Varimax rotation with Kaiser Normalization.

Supply chain resiliency. A second factor analysis was performed on eight items of various practices that can be

used for building supply chain resilience. The factor analysis extracted the two factors of flexibility and redundancy. All item loadings were greater than 0.50. The factor flexibility consists of auditing supplier processes, monitoring supplier financial conditions, and certifying suppliers. In contrast, the items loading on the second factor, redundancy, includes using dual or multiple supply sources, ensuring excess supplier capacity exists, having supply continuity plans in place, requiring suppliers to report disruptions, and having suppliers hold inventory to prevent stockouts. The two factors of flexibility and redundancy explain 55.7 % of the variance. The results of the rotated


factor matrix for supply chain resilience are summarized in Table 3. The Cronbach alpha reliability estimates for the factors flexibility and redundancy were 0.727 and 0.703 respectively, providing evidence that the internal consistency of these sets of scale items is satisfactory (Hair et al. 2006, p. 137).

TABLE 3

FACTOR ANALYSIS FOR SUPPLY RISK RESILIENCY

Items Flexibility Redundancy

Audit supplier’s internal processes and systems 0.875 -0.007 Monitor the financial condition of suppliers 0.810 0.120 Supplier certification programs 0.653 0.320 Dual or multiple supply sources -0.081 0.758 Ensure that excess supplier capacity exists to deal with unplanned increases in demand 0.273 0.721

Supply continuity / contingency plans 0.390 0.681 Require suppliers to immediately report all supply disruptions irrespective of their impact 0.033 0.626

Require suppliers to hold inventory for you to prevent stockouts 0.174 0.527

Cronbach 0.727 0.703 Response cue: To what degree do you use the following practices specifically to manage supply risk for the product identified? (5-point scale: 1 = do not use, 2 = low, 3 = moderate, 4 = high, 5 = very high). Extraction method: Principal component, Varimax rotation with Kaiser Normalization.

Disruption occurrence. The third factor analysis was conducted on the items measuring the frequency with

which firms experience the effects of supply disruptions. In this analysis only one eigenvalue greater than one was observed. All items had loadings greater than 0.40, and together form the construct of disruption occurrence. The factor extracted explains 61.0 % of the variance. The results of the rotated factor matrix for disruption occurrence are summarized in Table 4. The Cronbach alpha reliability estimate for the factor was high as 0.890, providing evidence that the internal consistency of the scale items is satisfactory (Hair et al. 2006, p. 137).

Summated factor scores were calculated as the means of the individual items. Descriptive statistics for all

summated variables, including means, standard deviations, and correlations, can be found in the Appendix.

Hierarchical Regression Results

Hierarchical multiple regression analysis and moderated multiple regression analysis, as shown in Table 5, were used to test the hypotheses. First, in order to test the linear relationships between the supply-side risk sources and supply disruption occurrence, we regressed disruption occurrence on the control variables (i.e., firm and non-domestic volume) in model 1 and estimated the parameters for the supply-side risk source main effects (i.e., supplier risk, supply market risk, and extended supply chain risk) in model 2.

The standardized regression coefficients for all three supply-side risk sources are statistically significant with

standardized parameter estimates of 0.150 (p < 0.01) for supplier risk, 0.320 (p < 0.001) for supply market risk, and -0.143 (p < 0.01) for extended supply chain risk. This provides support for H1a and H1b. Contrary to our expectations, the supply managers’ perceptions of extended supply chain risk are negatively related to disruption occurrence. Hence, the direction of the latter relationship is opposite as proposed in H1c.

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Next, the hypothesized interaction effects were tested based on the procedure proposed by Zedeck (1971). Prior to analysis, we transformed the variables so that the means of the transformed variables were zero (Cohen, Cohen, West, and Aiken 2003, pp. 261-262; Jaccard and Turrisi 2003, pp. 24-25). After mean centering of the variables and entering the two potential moderators (model 3), the following model 4 was estimated in order to examine the moderator effects of flexibility and redundancy on the relationship between supply-side risk sources and disruption occurrence.

TABLE 4

FACTOR ANALYSIS FOR DISRUPTION OCCURRENCE

Items Disruption occurrence

Operations disrupted due to a late delivery of this product 0.864 Operations disrupted due to a quality problem with this product 0.845 Expedited shipments to avoid a disruption due to a late delivery of this product 0.843

Late deliveries for this product 0.815 Unacceptable delivered quality for this product 0.801 Excess costs (e.g., premium freight, higher price from an alternate source) for this product due to a supplier's failure to perform 0.756

Use of an alternate source for this product because the primary source failed to perform 0.472

Cronbach 0.890

Response cue: Regarding the product chosen, how often has the following occurred during the past year? (5-point scale: 1 = never; 2 = rarely; 3 = occasionally; 4 = somewhat frequently; 5 = frequently). Extraction method: Principal component, Varimax rotation with Kaiser Normalization.

A significant increase of variance explained (R2) upon entering the interaction term would indicate the presence

of a moderated relationship (Jaccard and Turrisi 2003, pp. 11-12; Zedeck 1971). Model 4 supports our hypothesis that both flexibility and redundancy moderate some relationships between supply-side risk sources and disruption occurrence. Moderation is supported for three out of six relationships: Extended supply chains X flexibility with a standardized parameter estimate of -0.224 (p < 0.01); supply market X redundancy with a standardized parameter estimate of -0.198 (p < 0.001); and extended supply chains X redundancy with a standardized parameter estimate of 0.123 (p < 0.1). Model 4 explains 4 % additional variance upon introducing the interaction terms. Since there is consent in methodological studies on moderator research that significant interaction effects are difficult to detect and that effect sizes are very small (Aguinis, Beaty, Boik, and Pierce 2005; Champoux and Peters 1987; Chaplin 1991). This result constitutes an important contribution.

Having established the presence of three significant interactions between supply-side risk sources and supply

chain resiliency, it is warranted to plot the interactions and analyze their specific form following the procedure suggested by Aiken and West (1991, pp. 12-14) and Jaccard and Turrisi (2003, pp. 31-32). The three plots are depicted in Figure 2. The plots reveal several noteworthy insights. It can be seen that all three interactions are disordinal in nature as the regression lines of the relationship between supply-side risk sources and disruption cross over within the ranges of supply chain resiliency (Aiken and West 1991, pp. 22-23). This means the manipulated independent variables (flexibility and redundancy) have different consequences at the extremes of the predictor variable (supply market risk and extended supply chain risk).


TABLE 5

PARAMETER ESTIMATES OF REGRESSION MODELS

Dependent variable: Disruption occurrence Model 1 Model 2 Model 3 Model 4 Control variables

Construction -0.065 -0.086 -0.087 -0.096 Equip -0.065 -0.122 * -0.123 * -0.075 Aircraft 0.370 *** 0.243 *** 0.237 ** 0.235 ** MHE -0.063 -0.138 * -0.139 * -0.092 Non-domestic volume -0.040 -0.035 -0.036 -0.064

Main effects Supplier risk 0.150 * 0.156 * 0.168 * Supply market risk 0.320 *** 0.314 *** 0.318 *** Extended SC risk -0.143 * -0.135 * -0.135 *

Moderators Flexibility -0.006 -0.033 Redundancy -0.032 -0.006

Interaction effects Supplier X Flexibility 0.088 Supply market X Flexibility 0.088 Extended SC X Flexibility -0.224 *** Supplier X Redundancy 0.008 Supply market X Redundancy -0.198 ** Extended SC X Redundancy 0.123 +

Model summary F 14.858 *** 17.373 *** 13.869 *** 10.123 *** R2 0.204 0.326 0.327 0.367 R2 change 0.204 0.122 0.001 0.040 F value of R2 change 14.858 *** 17.372 *** 0.227 2.937 **

*** Significant at the 0.001 level. ** Significant at the 0.01 level. * Significant at the 0.05 level. + Significant at the 0.10 level.

Plot (a) indicates that as extended supply chain risk moves from low to high, disruption occurrence increases if the buying firm has little flexibility, and decreases if the buying firm has pursued flexible practices on a high level. Hence, flexibility can help firms to lessen negative consequences from extended supply chain risk. This provides (partial) support for H2a that flexibility moderates the relationship between perceived risks from extended supply chain risk and disruption occurrence.

The inspection of plot (b) reveals that if redundancy is pursued on a low level, disruptions occur less frequently

when there is high extended supply chain risk, and more frequently in cases of low extended supply chain risk. Furthermore, extended supply chain risk has a negligible effect under high levels of redundancy (zero slope). Considering the positive standard estimate of 0.123 for the interaction of extended supply chain X redundancy and the insights from plot (b) together indicates that high levels of redundancy do not support firms in mitigating extended supply chain risk. In view of the analysis in the previous paragraph, the implementation of flexible practices is a more powerful approach to limit the potential negative consequences stemming from extended supply chain risk sources.


FIGURE 2

PLOTS OF INTERACTIONS

(a) Interaction between Extended supply chain risk and Flexibility

(b) Interaction between Extended supply chain risk and Redundancy

Plot (c) indicates that as risks stemming from supply markets move from low to high, the frequency of disruption occurrence increases at a higher rate in the case of low levels of redundancy than in the case of high levels of redundancy. Therefore, firms who extensively implement redundant practices will less frequently experience the effects of supply disruption when supply market risks are perceived to be high. This provides (partial) support for H2b that redundancy moderates the relationship between perceived risks from supply market risk sources and disruption occurrence.

Overall, this in-depth analysis of the interaction effects shows that H2a and H2b are both partially supported.

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(c) Interaction between Supply market risk and Redundancy

MANAGERIAL IMPLICATONS

This study provides some confirmation, as well as new insights, into how managers perceive and manage

supply risk through resiliency practices. This section will first provide managerial implications with regard to the relationship between perceived supply risk and supply disruption occurrence. Then, implications associated with how supply chain resiliency practices moderate the effects of supply disruptions are examined.

Supply Risk Perceptions

The perceptions of supply management professionals are an important source to consider for understanding those threats that exist to supply continuity. We found that both the risk perceived from suppliers, as well as the risk that emanates from the overall market that suppliers’ compete, are good indicators of supply disruption occurrence. This infers that supply management professionals at the “grass-roots” level can serve as a key knowledge source of potential supply problems that can manifest into supply disruptions. With regard to the risk that exists from suppliers and the supply market, one mechanism that senior managers should consider implementing is the use of formal supply risk assessment tools that capture the knowledge that resides with supply management professionals. These tools should be disseminated to all individuals responsible for upstream supply chain management coordination. This can facilitate the capturing of key insights from supply management professionals, and communicated in real time to senior management what those potential problems are, in order to immediately deploy resources to prevent disruption occurrence, or to update and act-upon supply continuity plans that provide the firm a roadmap for action if the risk occurs. For example, if a buyer becomes aware that one of their key suppliers is beginning to experience financial difficulty, they can formally communicate this concern via a risk assessment to heighten organizational awareness before it results in a supply disruption.

An interesting finding in this study is that supply management professionals’ perceptions of risk from extended

supply chains are negatively associated with disruption occurrence. This may be due to the notion that prior studies have discovered that supply chain professionals believe that more lengthy supply chains are more susceptible to disruptions and risk (Manuj and Mentzer 2008; Wagner and Bode 2006; Zeng and Rosetti 2003). If there are significant perceptions of risk that exist with extended supply chains, supply management professionals may be more inclined to proactively take steps to reduce disruption occurrence. Supply managers will implement measures that go beyond what is necessary given the true likelihood of occurrence of disruptions stemming from extended supply chains. As a consequence, the higher the perception of extended supply chain as a risk source, the more they will do to mitigate such potential risks, and the firm will experience the effects of supply disruptions less frequently

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that are caused by extended supply chains. This is discussed in more detail in the next section with regard to flexibility and redundancy.

The “availability heuristics” (Kahneman, Slovic, and Tversky 1982) state that information that is easily

available and more prominent influences the perception, and consequently action, of human beings. Supply managers read in the popular press about the challenges of global sourcing, shortages in transportation capacity on certain trade lanes, or see pictures of natural disasters destroying logistics operations—risk sources associated with extended supply chains and how they can be perceived by managers to be significant. Recent examples in the media, such as the quality problems Mattel faced with regard to lead paint use on some of their toys (Lyles, Flynn, and Frohlich 2008; Tang 2008), may have an influence with regard to how purchasing professionals view supply risk from extended supply chains.

Supply Risk and Resiliency

After knowing that supply management professionals’ perceptions about risk are a vital source of information, supply management professionals need to know the appropriate steps to take in order to create resilient supply chains, given the source of risk perceived.

First, in our sample, we found that the supply resiliency practices of flexibility and redundancy do not moderate

the relationship between the perceived risk from suppliers and disruption occurrence, and that the supply resiliency practice of flexibility does not moderate the relationship between perceived supply market risk and disruption occurrence. The flexibility practices examined in this study, such as monitoring and auditing suppliers, may not either provide sufficient information about risks from suppliers, or at least the ability to make appropriate adjustments to avoid disruption occurrence in a timely manner. Auditing supplier processes, monitoring their financial condition, and certifying suppliers, can provide a picture of the current capabilities and performance of suppliers. However, it may not always be a predictor of future performance. Further, it may be necessary to go beyond just obtaining this knowledge, and instead become more proactive with suppliers to improve their processes in order to avoid supply disruptions.

The redundancy practices, such as having multiple suppliers, holding inventory and creating business continuity

plans, may not reduce how often disruptions occur. Although these practices are touted to “buy time” for organizations in case of disruption occurrence, they were found in this study not to have a moderating effect on risk from suppliers. One possible reason for this finding is that firms may overestimate the benefits of creating redundancy. For example, the literature on lean supply chain management argues that building in slack in the supply chain, such as through higher inventory levels, may actually hide problems that exist with suppliers and the supply chain (Trent 2008). Instead of building additional redundancy into the supply chain, purchasing professionals may instead consider investing in process improvement tools to strive towards preventing disruptions from occurring in the first place. For example, purchasing professionals may want to dedicate more time and effort during the supplier selection process (Trent 2008), to include investigating the potential and impact of supply disruptions, in order to better manage supply risk. The overall risk from suppliers can be reduced by only selecting firms with robust processes.

The flexibility practices may not weaken the relationship between perceived supply market risk and disruption

occurrence, because the flexibility practices investigated in this study can influence individual suppliers, but might not be able to influence the supply market as a whole. Depending on the structure of the supply market (e.g., number of suppliers, buyer-supplier power, competition among suppliers on the supply market), these measures to influence individual suppliers might not be effective. Concentrated actions of a number of buying firms would be necessary to influence supply market risk sources. For example, the shortages of steel due to high demands from certain industries and regions of the world resulted in supply disruptions for individual firms (Sawyer 2005). However, an individual firm would not have been able to lessen the supply market risk by implementing flexibility measures.

Second, in this research, we discovered that pursuing supply chain resiliency through redundancy moderates the

risk perceived from the supply market on disruption occurrence. However, flexibility was not found to moderate the relationship between supply market risk and disruption occurrence. Although flexibility may seem to be an attractive option by better understanding the suppliers in these markets, the buying firm in the end may not have an option with regard to where to source, if the incident affects the entire market and disrupts supply. However, we found that


disruption occurrence is reduced when implementing redundancy practices that “buy time” for the purchasing firm to find appropriate substitutes or short-term solutions to eventually recover. Therefore, it appears that supply chain redundancy has little to no benefits from supplier risk, but can ensure continuity if that risk exists throughout the overall supply market. This may especially be the case if substitutes are readily available when supplier risk is perceived as high, but from overall supply market risk, those substitutes may not be as readily available since the problem exists within the overall market itself.

Third, the biggest benefits to creating resiliency with flexibility appear to come from the risk that exists from

extended supply chains. The constant monitoring of suppliers to create flexibility and organizational knowledge of external threats risk can uncover problems that may exist when sourcing from far-off locations. However, we discovered that redundancy is a less powerful practice to reduce disruption risks. While low levels of implementation of these practices result in disruptions occurring less frequently when extended supply risk is perceived as greater, high levels of implementation do not reduce the frequency of disruptions. Similar to the argument provided with regard to supplier risk, the potential issue associated with redundancy is that problems may be hidden by actively using multiple suppliers and holding more inventory. Instead, firms should invest in being more knowledgeable about these supply chains to the risk that may exist.

LIMITATIONS AND CONCLUSIONS Limitations and Future Research Directions

As in all research, there are limitations with our current study. First, the target sample in this study captured risk perceptions, resiliency, and disruption occurrence from supply management professionals in three industries. Gathering data from personnel in five companies in three distinct industries limits the generalizability of our findings. Capturing data from respondents focusing on a specific purchase, establishing the unit of analysis as the purchased product, and controlling for firm effects does reduce threats to generalizability to some extent. For example, supply management professionals in Company Build acquire a diverse array of products from suppliers, such as resins, valves, stainless steel, power transmission products, and packaging, among many others. Each of these products has different sourcing and market characteristics, which subsequently varies its risk profile. However, future research should consider using a cross-sectional sample to capture risk from a greater variety of industries and supply chain dynamics.

A second limitation of the study is that we investigated the frequency of disruption occurrence and not the

actual costs associated with these disruptions. Future research may want to consider taking into consideration a more fine-grained analysis of supply disruption costs. These have, in part, been partially captured with regard to operational performance (Hendricks and Singhal 2005b), supply chain performance (Wagner and Bode 2008), and stock price valuation changes (Hendricks and Singhal 2003, 2005a). However, operational performance and supply chain performance covers performance-related aspects in a broader sense (e.g., capacity utilization or order fill rate), and stock price valuation captures many other factors beyond the effects of supply disruptions. The data such as actual costs associated with supply disruptions is difficult to capture by firms themselves, and if it is calculated, would most likely be considered as proprietary information. Therefore, the use of the case study method in which researchers have close relations with a firm may begin to shed insight as to what the actual costs of disruptions are, as well as provide rich, contextual factors examining a trail of evidence as to how these disruptions manifest.

The third limitation is that we gathered supply disruption data with a survey instrument deployed at one specific

point in time. Therefore, we cannot infer what happens over an extended period of time. This would be an important next step for future study due to several reasons. First, while we contribute to a better understanding of supply disruption occurrence, we have not studied how firms react to an experienced supply disruption (i.e., what happens after a supply disruption strikes the firm?). Second, the learning literature suggests that a supply disruption stimulates organizational learning and adaptation as firms attempt to ameliorate the impact of future potential disruptions. Organizational learning effects are also not captured in the present study. Third, the perception of purchasing managers will be shaped over time. Their reactions to the accumulated experience with supply disruptions will differ—depending on whether they have been exposed to supply disruptions from suppliers, the supply market or the extended supply chain just recently, or over a longer period of time. The investigation of such effects is up to future research. Fourth, organizational culture, which provides norms for behavior and for common


practice in the organization, is developed over time. Cultural nuances may be an important factor for explaining firms’ vulnerability to supply-side disruptions as well as firm responses to supply disruptions. These avenues for future research require researchers to go beyond cross-sectional research designs and study firms and purchasing managers in this context over time (i.e. through longitudinal data collection).

One additional limitation of our study concerns the restricted number of items measuring the construct

flexibility. As noted by Sheffi (2005), supply chain flexibility consists of building organizational and interorganizational capabilities to sense and respond quickly to supply disruption threats. In this study, we constrained our investigation to flexibility practices oriented to the supply management function because this was the directional focus of our study. Future research may want to investigate supply chain flexibility from a more holistic supply chain perspective. This would include incorporating the additional facets of supply chain flexibility of conversion, distribution and customer-facing activities, control systems, and corporate culture (Sheffi and Rice 2005).

Conclusion

Risk is ever-present in all firms’ supply chains. This study has investigated the validity of risk perceptions with regard to supply disruption occurrence, as well as the moderating effect of supply chain resiliency practices on disruption occurrence. Not all risk is the same, nor should supply disruptions be managed using the same tools. Understanding the source of risk is important for creating a tailored strategy for reducing the occurrence of supply disruptions, such as the use of flexibility in order to create resiliency from risk that originates from extended supply chains. In other circumstances, specifically when risk stems from forces outside the control of supply chain participants (i.e., supply market risk), it is imperative to insulate themselves, at least in the short-term, from the effects of a disruption occurrence by using practices that create redundancy in the supply chain. The study of risk is inherently a very broad subject, with many factors and perspectives. This research has focused on one manifestation of risk—supply disruptions, and how firms can manage that risk by creating supply chain resiliency. By understanding how supply management professionals and organizations view risk, as well as the tools those managers deploy, we can, with continued study, better tease out and propose to practicing managers how firms can better manage that risk in order to reduce disruption occurrence and improve overall supply chain practice.

APPENDIX

CORRELATIONS AND DESCRIPTIVE STATISTICS

Variables (1) (2) (3) (4) (5) (6) (1) Supplier risk 1.000 (2) Supply market risk 0.556** 1.000 (3) Extended SC risk 0.555** 0.466** 1.000 (4) Flexibility 0.238** 0.161** 0.136* 1.000 (5) Redundancy 0.166** -0.030 0.267** 0.385 ** 1.000 (6) Disruption occurrence 0.257** 0.366** 0.013 0.160 ** -0.126 * 1.000 Mean 3.16 3.19 2.65 2.94 2.98 2.44 Standard deviation 0.93 0.95 0.91 0.95 0.75 0.84 ** Significant at the 0.01 level. * Significant at the 0.05 level.

REFERENCES

Aguinis, Herman, James C. Beaty, Robert J. Boik, and Charles A. Pierce (2005), “Effect Size and Power in Assessing Moderating Effects of Categorical Variables Using Multiple Regression: A 30-Year Review,” Journal of Applied Psychology, Vol. 90, No. 1, pp. 94-107.


Aiken, Leona S. and Stephen G. West (1991), Multiple Regression: Testing and Interpreting Interactions, Newbury Park, CA: Sage Publications, Inc. Bazermann, Max H. (2005), Judgment in Managerial Decision Making, 6th ed., New York: John Wiley & Sons. Brindley, Clare (2004), Supply Chain Risk, Burlington, VT: Ashgate. Carbone, James (2000), “Computer Industry Buyers Deal With Tight Supplies,” Purchasing, Vol. 129, No. 5, pp. 45-48. Carter, Craig R., Lutz Kaufmann, and Alex Michel (2007), “Behavioral Supply Management: A Taxonomy of Judgment and Decision-Making Biases,” International Journal of Physical Distribution and Logistics Management, Vol. 37, No. 8, pp. 631-669. Champoux, Joseph and William S. Peters (1987), “Form, Effect Size, and Power in Moderated Regression Analysis,” Journal of Occupational Psychology, Vol. 60, No. 3, pp. 243-255. Chaplin, William F. (1991), “The Next Generation of Moderator Research in Personality Psychology,” Journal of Personality, Vol. 59, No. 2, pp. 143-178. Chopra, Sunil and ManMohan S. Sodhi, (2004), “Managing Risk to Avoid Supply Chain Breakdown,” MIT Sloan Management Review, Vol. 46, No. 1, pp. 53-61. Christopher, Martin and Helen Peck (2004), “Building the Resilient Supply Chain,” The International Journal of Logistics Management, Vol. 15, No. 2, pp. 1-13. Cohen, Jacob, Patricia W. Cohen, Stephen G. West, and Leona S. Aiken (2003), Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd ed., Mahwah, NJ: Lawrence Erlbaum Associates. Craighead, Christopher W., Jennifer Blackhurst, M. Johnny Rungtusanatham, and Robert B. Handfield (2007), “The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities,” Decision Sciences, Vol. 38, No. 1, pp. 131-156. Cronbach, Lee J. (1951), “Coefficient Alpha and the Internal Structure of Tests,” Psychometrika, Vol. 16, No. 3, pp. 297-334. Damodaran, Aswath (2007), Strategic Risk Taking: A Framework for Risk Management, Philadelphia, PA: Wharton School Publishing. de Waart, Dirk (2006), “Getting Smart About Risk,” Supply Chain Management Review, Vol. 10, No. 8, pp. 27-33. Dillman, Don A. (2007), Mail and Internet Surveys: The Tailored Design Method—Update with New Internet, Visual, and Mixed-Mode Guide, 2nd ed., New York: John Wiley & Sons. Eisenhardt, Kathleen M. (1989), “Building Theories from Case Study Research,” Academy of Management Review, Vol. 14, No. 4, pp. 532-550. Ellram, Lisa M. (1996), “The Use of the Case Study Method in Logistics Research,” Journal of Business Logistics, Vol. 17, No. 2, pp. 93-138. Gilbert, Gregory A. and Michael A. Gips (2000), “Supply-Side Contingency Planning,” Security Management, Vol. 44, No. 3, pp. 70-74. Hair, Joseph F., Bill Black, Barry Babin, Rolph E. Anderson, and Ronald L. Tatham (2006), Multivariate Data Analysis, 6th ed., Upper Saddle River, NJ: Pearson Prentice Hall.


Hallikas, Jukka, Iris Karvonen, Urho Pulkkinen, Veli-Matti Virolainen, and Markku Tuominen (2004), “Risk Management Processes in Supplier Networks,” International Journal of Production Economics, Vol. 90, No. 1, pp. 47-58. Handfield, Robert B. and Kevin P. McCormack (2007), Supply Chain Risk Management: Minimizing Disruption in Global Sourcing, Boca Raton, FL: Taylor and Francis. Harland, Christine M., Richard Brenchley, and Helen Walker (2003), “Risk in Supply Networks,” Journal of Purchasing & Supply Management, Vol. 9, No. 1, pp. 51-62. Hendricks, Kevin B. and Vinod R. Singhal (2005a), “An Empirical Analysis of the Effects of Supply Chain Disruptions on Long-Run Stock Price Performance and Equity Risk of the Firm,” Production and Operations Management, Vol. 14, No. 1, pp. 35-52. Hendricks, Kevin B. and Vinod R. Singhal (2005b), “Association Between Supply Chain Glitches and Operating Performance,” Management Science, Vol. 51, No. 5, pp. 695-711. Hendricks, Kevin B. and Vinod R. Singhal (2003), “The Effects of Supply Chain Glitches on Shareholder Wealth,” Journal of Operations Management, Vol. 21, No. 5, pp. 501-522. Jaccard, James and Robert Turrisi (2003), Interaction Effects in Multiple Regression, 2nd ed., Newbury Park, CA: Sage Publications, Inc. Jüttner, Uta (2005), “Supply Chain Risk Management—Understanding the Business Requirements from a Practitioner Perspective,” The International Journal of Logistics Management, Vol. 16, No. 1, pp. 120-141. Kahneman, Daniel and Amos Tversky (1972), “Subjective Probability: A Judgment of Representativeness,” Cognitive Psychology, Vol. 3, No. 3, pp. 430-454. Kahneman, Daniel, Paul Slovic, and Amos Tversky (eds.) (1982), Judgment Under Uncertainty: Heuristics and Biases, Cambridge, UK: Cambridge University Press. Keren, Gideon (1990), “Cognitive Aids and Debiasing Methods. Can Cognitive Pills Cure Cognitive Ills?” in Jean-Paul Caverni and Jean-Marc Fabre (eds.), Cognitive Biases, Amsterdam: North-Holland, pp. 523-555. Lyles, Marjorie, Barbara B. Flynn, and Mark Frohlich (2008), “All Supply Chains Don’t Flow Through: Understanding Supply Chain Issues in Product Recalls,” Management and Organization Review, Vol. 4, No. 2, pp. 167-182. Manuj, Ila and John T. Mentzer (2008), “Global Supply Chain Risk Management,” Journal of Business Logistics, Vol. 29, No. 1, pp. 133-155. Miller, George A. (1956), “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information,” The Psychological Review, Vol. 63, No. 2, pp. 81-97. Milne, Richard (2009), “Early Warnings in the Supply Chain,” Financial Times Europe, No. 36957 (March 24), p. 10. Mitchell, Vincent-Wayne (1995), “Organizational Risk Perception and Reduction—A Literature Review,” British Journal of Management, Vol. 6, No. 2, pp. 115-133. Norrman, Andreas and Ulf Jansson (2004), “Ericsson’s Proactive Supply Chain Risk Management Approach after a Serious Sub-Supplier Accident,” International Journal of Physical Distribution and Logistics Management, Vol. 34, No. 5, pp. 434-456.


Renn, Ortwin (2004), “Perception of Risks,” The Geneva Papers on Risk and Insurance, Vol. 29, No. 1, pp. 102-114. Ritchie, Bob and Clare Brindley (2007), “An Emergent Framework for Supply Chain Risk Management and Performance Measurement,” Journal of the Operational Research Society, Vol. 58, No. 11, pp. 1398-1411. Sawyer, Christopher A. (2005), “Riding the Steel Roller Coaster,” Automotive Design & Production, Vol. 117, No. 2, p. 48. Schaffer, Bryan S. and Christine M. Riordan (2003), “A Review of Cross-Cultural Methodologies for Organizational Research: A Best-Practices Approach,” Organizational Research Methods, Vol. 6, No. 2, pp. 169-215. Schoenherr, Tobias, V. M. Rao Tummala, and Thomas P. Harrison (2008), “Assessing Supply Chain Risks with the Analytic Hierarchy Process: Providing Decision Support for the Offshoring Decision by a US Manufacturing Company,” Journal of Purchasing & Supply Management, Vol. 14, No. 2, pp. 100-111. Sheffi, Yossi (2005), The Resilient Enterprise, Cambridge, MA: MIT Press. Sheffi, Yossi and James B. Rice, Jr. (2005), “A Supply Chain View of the Resilient Enterprise,” MIT Sloan Management Review, Vol. 47, No. 1, pp. 41-48. Sitkin, Sim B. and Amy L. Pablo (1992), “Reconceptualizing the Determinants of Risk Behavior,” Academy of Management Review, Vol. 17, No. 1, pp. 9-38. Slovic, Paul, Baruch Fischhoff, and Sarah Lichtenstein (1982), “Facts Versus Fears: Understanding Perceived Risk,” in Daniel Kahneman, Paul Slovic, and Amos Tversky (eds.), Judgment Under Uncertainty: Heuristics and Biases, Cambridge, UK: Cambridge University Press, pp. 463-489. Spekman, Robert E. and Edward W. Davis (2004), “Risky Business: Expanding the Discussion on Risk and the Extended Enterprise,” International Journal of Physical Distribution and Logistics Management, Vol. 34, No. 5, pp. 414-433. Svensson, Göran (2000), “A Conceptual Framework for the Analysis of Vulnerability in Supply Chains,” International Journal of Physical Distribution and Logistics Management, Vol. 30, No. 9, pp. 731-750. Tang, Christopher S. (2008), “Making Products Safe: Process and Challenges,” International Commerce Review, Vol. 8, No. 1, pp. 48-55. Tang, Christopher S. (2006), “Perspectives in Supply Chain Risk Management,” International Journal of Production Economics, Vol. 103, No. 2, pp. 451-488. Thaler, Richard (1985), “Mental Accounting and Consumer Choice,” Marketing Science, Vol. 4, No. 3, pp. 199-214. Tomlin, Brian (2006), “On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks,” Management Science, Vol. 52, No. 5, pp. 639-657. Trent, Robert J. (2008), End-to-End Lean Management: A Guide to Complete Supply Chain Improvement, Fort Lauderdale, FL: J. Ross. Trevelen, Marc and Sharon Bergman Schweikhart (1988), “A Risk/Benefit Analysis of Sourcing Strategies: Single vs. Multiple Sourcing,” Journal of Operations Management, Vol. 7, No. 4, pp. 93-114. Vestring, Till, Ted Rouse, and Uwe Reinert (2005), “Hedge Your Offshoring Bets,” MIT Sloan Management Review, Vol. 46, No. 3, pp. 27-29.


Wagner, Stephan M. and Christoph Bode (2008), “An Empirical Examination of Supply Chain Performance along Several Dimensions of Risk,” Journal of Business Logistics, Vol. 29, No. 1, pp. 307-325. Wagner, Stephan M. and Christoph Bode (2006), “An Empirical Investigation into Supply Chain Vulnerability,” Journal of Purchasing & Supply Management, Vol. 12, No. 6, pp. 301-312. Wagner, Stephan M. and Jean L. Johnson (2004), “Configuring and Managing Strategic Supplier Portfolios,” Industrial Marketing Management, Vol. 33, No. 8, pp. 717-730. Waring, Alan and A. Ian Glendon (1998), Managing Risk: Critical Issues for Survival and Success into the 21st Century, London: International Thomson Business Press. Watson, Andrew and Terence Rodgers (1998), “The Human Nature of Management Consulting: Judgment and Expertise,” Managerial & Decision Economics, Vol. 19, No. 7/8, pp. 495-503. Winegarner, Richard M. and Robert O. Johnson (2007), Polysilicon 2007, www.solarinsights.com/summary.htm, accessed on November 25, 2008. Yin, Robert K. (2009), Case Study Research: Design and Methods, 4th ed., Thousand Oaks, CA: Sage Publications, Inc. Zedeck, Sheldon (1971), “Problems with the Use of ‘Moderator’ Variables,” Psychological Bulletin, Vol. 76, No. 4, pp. 295-310. Zeng, Amy Z. and Christian Rossetti (2003), “Developing a Framework for Evaluating the Logistics Costs in Global Sourcing Processes: An Implementation and Insights,” International Journal of Physical Distribution and Logistics Management, Vol. 33, No. 9, pp. 785-803. Zsidisin, George A. (2003), “Managerial Perceptions of Supply Risk,” Journal of Supply Chain Management, Vol. 39, No. 1, pp. 14-25. Zsidisin, George A., Steven A. Melnyk, and Gary L. Ragatz (2005), “An Institutional Theory Perspective of Business Continuity Planning for Purchasing and Supply Management,” International Journal of Production Research, Vol. 43, No. 16, pp. 3401-3420. Zsidisin, George A. and Bob Ritchie (2009), Supply Chain Risk: A Handbook of Assessment, Management and Performance, New York: Springer.

ABOUT THE AUTHORS

George A. Zsidisin, C.P.M. (Ph.D. Arizona State University) is an Associate Professor at the Department of Management, Bowling Green State University. He has published over 40 articles in both academic and practitioner journals, as well as given numerous presentations to companies, groups, and conferences throughout North America and Europe. His research interests include supply risk and its management, early supplier involvement in new product development, and the role of information technology in supply management.

Stephan M. Wagner (Ph.D. University of St. Gallen) is a Professor and holds the Kuehne-Foundation

sponsored Chair of Logistics Management at the Swiss Federal Institute of Technology Zurich. His research in supply chain management focuses particularly on strategy, networks, relationships, behavioral issues, risk and innovation. He is a vivid researcher, presents regularly at international conferences, and has published over 100 academic and professional articles, as well as ten books. Contact author: George Zsidisin; E-mail: [email protected]

do perceptions become reality? the moderating role of supply chain resiliency on disruption...

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